Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network
A Case Study
Bibliographic Data
| ID | 15499342 |
|---|---|
| Authors | Chao Liu (0000-0001-9257-4519, Qingdao University of Technology), Mingshuang Xu (0009-0005-9571-3816, Qingdao University of Technology), Yufeng Liu (0009-0009-1593-6753, Qingdao University of Technology), Xuefei Li (0000-0001-5074-9245, Qingdao University of Technology), Zonglin Pang (Qingdao University of Technology), Sheng Miao (0000-0001-6176-3624, Qingdao University of Technology, corresponding author) |
| Year | 2022 |
| Volume | 19 |
| Issue | 23 |
| Pages | 15612-15612 |
| Publication date | 2022-11-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph192315612 |
| PMID | 36497698 |
| OpenAlex | W4309934724 |
| Language | EN |
| References cited | 34 |
Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions
Artificial neural network · Groundwater · Hydrogeology · Hydrology (agriculture · Machine learning · Nitrate · Sustainability · Sustainable Management · Water quality · Water resource management · Computer Science · Environmental Science · Groundwater and Watershed Analysis · Hydrological Forecasting Using AI · Water Quality Monitoring Technologies · Ecology · Geology
| Highly cited | No |
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